# Copyright (c) 2025 Intel Corporation
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#      http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.


import numpy as np
import onnx

from nncf.common.factory import TModel
from nncf.common.graph.graph import NNCFGraph
from nncf.common.graph.transformations.commands import TargetType
from nncf.common.graph.transformations.layout import TransformationLayout
from nncf.common.tensor_statistics.aggregator import StatisticsAggregator
from nncf.common.tensor_statistics.statistic_point import StatisticPointsContainer
from nncf.common.utils.backend import BackendType
from nncf.experimental.common.tensor_statistics.collectors import TensorCollector
from nncf.experimental.common.tensor_statistics.statistics import TensorStatistic
from nncf.onnx.graph.node_utils import get_input_edge
from nncf.onnx.graph.node_utils import get_input_edges_mapping
from nncf.onnx.graph.onnx_helper import get_name_to_node_map
from nncf.onnx.graph.transformations.commands import ONNXOutputInsertionCommand
from nncf.onnx.graph.transformations.commands import ONNXTargetPoint
from nncf.tensor import Tensor


class ONNXStatisticsAggregator(StatisticsAggregator):
    BACKEND: BackendType = BackendType.ONNX

    def collect_statistics(self, model: onnx.ModelProto, graph: NNCFGraph) -> None:
        self.input_edges_mapping = get_input_edges_mapping(graph)
        self.node_mapping = get_name_to_node_map(model)
        self._registered_weights = set()
        super().collect_statistics(model, graph)

    def _register_statistics(self, outputs: dict[str, Tensor], statistic_points: StatisticPointsContainer) -> None:
        for _, statistic_point, tensor_collector in statistic_points.get_tensor_collectors():
            target_point = statistic_point.target_point
            port_id = target_point.port_id

            if target_point.target_node_name in self.input_edges_mapping:  # Input case
                edge_name = get_input_edge(
                    target_point.target_node_name,
                    self.input_edges_mapping,
                    self.node_mapping,
                )
            elif target_point.type == TargetType.POST_LAYER_OPERATION:
                node = self.node_mapping[target_point.target_node_name]
                edge_name = node.output[port_id]
            elif target_point.type in [TargetType.PRE_LAYER_OPERATION, TargetType.OPERATION_WITH_WEIGHTS]:
                node = self.node_mapping[target_point.target_node_name]
                edge_name = node.input[port_id]
            else:
                RuntimeError(f"Unsupported target point type for statistic aggregator: {target_point.type}")

            input_info = []
            for reducer in tensor_collector.reducers:
                input_info.append((hash(reducer), [edge_name]))

            target_inputs = TensorCollector.get_tensor_collector_inputs(outputs, input_info)
            tensor_collector.register_inputs(target_inputs)

    def _get_transformation_layout_extra_outputs(
        self, statistic_points: StatisticPointsContainer
    ) -> TransformationLayout:
        transformation_layout = TransformationLayout()
        transformation_commands = []
        for _statistic_points in statistic_points.values():
            for _statistic_point in _statistic_points:
                transformation_commands.append(
                    ONNXOutputInsertionCommand(_statistic_point.target_point, self.input_edges_mapping)
                )
        for transformation_command in transformation_commands:
            transformation_layout.register(transformation_command)

        return transformation_layout

    @staticmethod
    def _get_merged_statistic_points(
        statistic_points: StatisticPointsContainer, model: TModel, graph: NNCFGraph
    ) -> StatisticPointsContainer:
        # TODO: migrate to experimental statistic collector and use common merging algorithm
        return statistic_points

    @staticmethod
    def _process_outputs(outputs: dict[str, np.ndarray]) -> dict[str, Tensor]:
        return {n: Tensor(v) for n, v in outputs.items()}

    def _get_statistics_key(self, statistics: TensorStatistic, target_point: ONNXTargetPoint) -> str:
        """
        Returns key of statistics.

        :param statistics: Statistics value.
        :param target_point: Statistics target point.
        :return: Statistics key.
        """
        target_point_id = f"{target_point.target_node_name}_{target_point.type}_{target_point.port_id}"
        return f"{statistics.__class__.__name__}_{target_point_id}"
